#Libraries
library(tidyverse)
library(readxl)
library(modelr)
library(sjstats)
library(car)
#Files
volumes_file <- "~/Desktop/ELS/income_TBM/data/final_data/inr_volumes_20190218.xlsx"
age_match_ids_file <- "~/Desktop/ELS/income_TBM/data/final_data/age_matched_ids.csv"
vols <-
read_xlsx(volumes_file, sheet = "CSX Volumes") %>%
rename(
pos_rSTG_cs = Pos_10, #superior temporal gyrus
pos_lIFG_cs = Pos_11, #inferior frontal gyrus
pos_lLOC_cs = Pos_12, #lateral occipital gyrus
pos_rMFG_cs = Pos_14, #middle frontal gyrus
pos_rSFG_cs_dlPFC = Pos_15, #superior frontal gyrus
pos_rPCG = Pos_16, #postcentral gyrus,
pos_rSFG_cs_motor = Pos_17,
pos_rPTR_cs = Pos_18, #posterior thalamic radiation,
pos_rMB_cs = Pos_19, #midbrain,
pos_rCV_cs = Pos_20, #cerebellar vermis,
neg_FG_cs = Neg_7, #fusiform gyrus,
neg_lThal_cs = Neg_8, #thalamus
neg_rHipp_cs = Neg_9, #hippocampus/CGH
neg_lHipp_cs = Neg_10, #hippocampus/CGH/fusiform gyrus,
neg_rThal_cs = Neg_11, #thalamus
neg_lAG_cs = Neg_12 #angular gyrus
) %>%
left_join(
read_xlsx(volumes_file, sheet = "Long Volumes") %>%
rename(
pos_rSPL_lg = Pos_4, #superior parietal lobule
pos_rLG_lg = Pos_5, #lingual gyrus,
pos_lITG_lg = Pos_6, #inferior temporal gyrus,
pos_lLG_lg = Pos_7, #lingual gyrus,
neg_rHipp_lg = Neg_7, #hippcampus/CGH,
neg_lSLF_lg = Neg_8, #superior longitudinal fasciculus,
neg_lSFG_lg = Neg_9, #superior frontal gyrus
neg_rSFG_lg = Neg_10, #superior frontal gyrus
neg_lCerebel_lg = Neg_11, #cerebellum
neg_rPTR_lg = Neg_12 #posterior thalamic radiation
) %>%
select(-T1_ICV, -T1_Age, -White, -INR_Sex, -Sex, -INR),
by = "Subject"
) %>%
rename(ID = Subject) %>%
mutate(Male = as.factor(Sex)) %>%
left_join(
read_csv(age_match_ids_file) %>%
rename(
ID = ELS_ID
) %>%
mutate(
age_matched = 1
),
by = "ID"
)
-
/
-
/
Parsed with column specification:
cols(
ELS_ID = [32mcol_double()[39m
)
vols_match <-
vols %>%
filter(age_matched == 1)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm1Ma <- lm(pos_rCV_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm1Ma)
Call:
lm(formula = pos_rCV_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.16663 -0.05848 -0.02213 0.05670 0.19117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.5339084 1.7176767 -0.893 0.37650
sqrt(INR) 0.4495654 0.1330512 3.379 0.00149 **
Male1 0.5666757 0.2163699 2.619 0.01190 *
T1_Age 0.0699678 0.0567636 1.233 0.22398
White 0.0196378 0.0303167 0.648 0.52036
T1_ICV 0.0002547 0.0011223 0.227 0.82150
sqrt(INR):Male1 -0.5140601 0.2048626 -2.509 0.01568 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1026 on 46 degrees of freedom
Multiple R-squared: 0.2274, Adjusted R-squared: 0.1267
F-statistic: 2.257 on 6 and 46 DF, p-value: 0.05426
std_beta(lm1Ma)
summary(lm2M)
Call:
lm(formula = pos_rMB_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols)
Residuals:
Min 1Q Median 3Q Max
-0.38276 -0.08212 -0.00292 0.08828 0.30981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4634134 0.9866559 0.470 0.639314
sqrt(INR) 0.3313063 0.1233348 2.686 0.008101 **
Male1 0.5777801 0.1554662 3.716 0.000291 ***
T1_Age 0.0153109 0.0105009 1.458 0.147064
White -0.0400713 0.0209360 -1.914 0.057662 .
T1_ICV -0.0008724 0.0007268 -1.200 0.232055
sqrt(INR):Male1 -0.5123634 0.1473558 -3.477 0.000676 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1237 on 140 degrees of freedom
Multiple R-squared: 0.1445, Adjusted R-squared: 0.1079
F-statistic: 3.942 on 6 and 140 DF, p-value: 0.001131
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm2Ma <- lm(pos_rMB_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm2Ma)
Call:
lm(formula = pos_rMB_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.34515 -0.06850 0.02252 0.06030 0.27792
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.999029 2.139785 1.869 0.0680 .
sqrt(INR) 0.246811 0.165748 1.489 0.1433
Male1 0.527849 0.269542 1.958 0.0563 .
T1_Age -0.090490 0.070713 -1.280 0.2071
White -0.079966 0.037767 -2.117 0.0397 *
T1_ICV -0.002538 0.001398 -1.815 0.0761 .
sqrt(INR):Male1 -0.449680 0.255206 -1.762 0.0847 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1278 on 46 degrees of freedom
Multiple R-squared: 0.2784, Adjusted R-squared: 0.1843
F-statistic: 2.958 on 6 and 46 DF, p-value: 0.01584
std_beta(lm2Ma)
outlierTest(lm3F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm3Ma <- lm(pos_rPTR_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm3Ma)
Call:
lm(formula = pos_rPTR_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.218653 -0.070921 0.000017 0.063801 0.237211
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.6772470 2.0470563 -0.819 0.41681
sqrt(INR) 0.4601431 0.1585650 2.902 0.00567 **
Male1 0.7491131 0.2578608 2.905 0.00563 **
T1_Age 0.0447676 0.0676486 0.662 0.51142
White -0.0174304 0.0361302 -0.482 0.63179
T1_ICV 0.0005576 0.0013375 0.417 0.67873
sqrt(INR):Male1 -0.6152283 0.2441468 -2.520 0.01527 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1222 on 46 degrees of freedom
Multiple R-squared: 0.3139, Adjusted R-squared: 0.2244
F-statistic: 3.507 on 6 and 46 DF, p-value: 0.006116
std_beta(lm3Ma)
outlierTest(lm4F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm4Ma <- lm(pos_rSFG_cs_motor ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm4Ma)
Call:
lm(formula = pos_rSFG_cs_motor ~ sqrt(INR) * Male + T1_Age +
White + T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.34653 -0.09981 0.00572 0.09935 0.35439
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.996834 2.630089 -0.759 0.45159
sqrt(INR) 0.419811 0.203727 2.061 0.04502 *
Male1 0.952567 0.331303 2.875 0.00610 **
T1_Age 0.025999 0.086916 0.299 0.76619
White -0.045970 0.046421 -0.990 0.32721
T1_ICV 0.000800 0.001718 0.466 0.64374
sqrt(INR):Male1 -0.957334 0.313684 -3.052 0.00377 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1571 on 46 degrees of freedom
Multiple R-squared: 0.2228, Adjusted R-squared: 0.1215
F-statistic: 2.198 on 6 and 46 DF, p-value: 0.06014
std_beta(lm4Ma)
outlierTest(lm5F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm5Ma <- lm(pos_rPCG ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm5Ma)
Call:
lm(formula = pos_rPCG ~ sqrt(INR) * Male + T1_Age + White + T1_ICV,
data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.24765 -0.12809 0.01160 0.08905 0.36692
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5665622 2.6098063 0.600 0.5513
sqrt(INR) 0.4621886 0.2021556 2.286 0.0269 *
Male1 0.5792700 0.3287485 1.762 0.0847 .
T1_Age -0.1065636 0.0862456 -1.236 0.2229
White -0.0531768 0.0460626 -1.154 0.2543
T1_ICV -0.0005821 0.0017052 -0.341 0.7344
sqrt(INR):Male1 -0.5274940 0.3112645 -1.695 0.0969 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1558 on 46 degrees of freedom
Multiple R-squared: 0.1929, Adjusted R-squared: 0.08764
F-statistic: 1.832 on 6 and 46 DF, p-value: 0.1136
std_beta(lm5Ma)
outlierTest(lm6F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm6Ma <- lm(pos_rSFG_cs_dlPFC ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm6Ma)
Call:
lm(formula = pos_rSFG_cs_dlPFC ~ sqrt(INR) * Male + T1_Age +
White + T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.30245 -0.12260 -0.00053 0.13019 0.37256
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.976568 2.935849 0.333 0.74092
sqrt(INR) 0.646413 0.227411 2.842 0.00665 **
Male1 0.664792 0.369819 1.798 0.07880 .
T1_Age 0.055335 0.097020 0.570 0.57122
White 0.011561 0.051817 0.223 0.82444
T1_ICV -0.001726 0.001918 -0.900 0.37294
sqrt(INR):Male1 -0.631426 0.350151 -1.803 0.07789 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1753 on 46 degrees of freedom
Multiple R-squared: 0.1795, Adjusted R-squared: 0.07246
F-statistic: 1.677 on 6 and 46 DF, p-value: 0.1482
std_beta(lm6Ma)
outlierTest(lm7F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm7Ma <- lm(pos_rMFG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm7Ma)
Call:
lm(formula = pos_rMFG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.35292 -0.11674 -0.00352 0.11356 0.30581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.463282 2.922855 0.159 0.8748
sqrt(INR) 0.512866 0.226404 2.265 0.0282 *
Male1 0.695032 0.368182 1.888 0.0654 .
T1_Age 0.106856 0.096591 1.106 0.2744
White -0.013195 0.051588 -0.256 0.7993
T1_ICV -0.001703 0.001910 -0.892 0.3772
sqrt(INR):Male1 -0.651147 0.348601 -1.868 0.0682 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1745 on 46 degrees of freedom
Multiple R-squared: 0.1375, Adjusted R-squared: 0.02503
F-statistic: 1.222 on 6 and 46 DF, p-value: 0.3123
std_beta(lm7Ma)
outlierTest(lm8M)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm8Ma <- lm(pos_lLOC_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm8Ma)
Call:
lm(formula = pos_lLOC_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.24193 -0.08647 -0.02296 0.09096 0.28634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.150520 2.216343 -0.068 0.946
sqrt(INR) 0.312772 0.171678 1.822 0.075 .
Male1 0.405342 0.279185 1.452 0.153
T1_Age -0.066246 0.073243 -0.904 0.370
White -0.009608 0.039118 -0.246 0.807
T1_ICV 0.000470 0.001448 0.325 0.747
sqrt(INR):Male1 -0.426981 0.264337 -1.615 0.113
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1323 on 46 degrees of freedom
Multiple R-squared: 0.1322, Adjusted R-squared: 0.01897
F-statistic: 1.168 on 6 and 46 DF, p-value: 0.34
std_beta(lm8Ma)
contrasts(vols$Male) = c(0, 1)
# SE female
lm9F <- lm(pos_lIFG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols)
summary(lm9F)
Call:
lm(formula = pos_lIFG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols)
Residuals:
Min 1Q Median 3Q Max
-0.56310 -0.13836 0.01717 0.14774 0.40807
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.811725 1.591927 1.138 0.257035
sqrt(INR) -0.283084 0.130205 -2.174 0.031376 *
Male1 -0.874929 0.250265 -3.496 0.000633 ***
T1_Age -0.013186 0.016904 -0.780 0.436678
White 0.056685 0.033702 1.682 0.094808 .
T1_ICV -0.001107 0.001170 -0.947 0.345508
sqrt(INR):Male1 0.823775 0.237209 3.473 0.000686 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1992 on 140 degrees of freedom
Multiple R-squared: 0.09682, Adjusted R-squared: 0.05812
F-statistic: 2.501 on 6 and 140 DF, p-value: 0.02492
std_beta(lm9F)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm9Ma <- lm(pos_lIFG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm9Ma)
Call:
lm(formula = pos_lIFG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.3695 -0.1374 -0.0204 0.1234 0.3647
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.258397 2.991726 1.089 0.28177
sqrt(INR) 0.629328 0.231739 2.716 0.00929 **
Male1 1.122858 0.376858 2.980 0.00460 **
T1_Age 0.023964 0.098867 0.242 0.80956
White 0.046655 0.052803 0.884 0.38152
T1_ICV -0.003203 0.001955 -1.638 0.10816
sqrt(INR):Male1 -1.036832 0.356815 -2.906 0.00562 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1787 on 46 degrees of freedom
Multiple R-squared: 0.2405, Adjusted R-squared: 0.1414
F-statistic: 2.428 on 6 and 46 DF, p-value: 0.04021
std_beta(lm9Ma)
outlierTest(lm10F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm10Ma <- lm(pos_rSTG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm10Ma)
Call:
lm(formula = pos_rSTG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.36278 -0.10451 0.00383 0.10380 0.37672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0478521 2.9143570 0.016 0.9870
sqrt(INR) 0.6657509 0.2257461 2.949 0.0050 **
Male1 0.8892313 0.3671117 2.422 0.0194 *
T1_Age -0.1136426 0.0963101 -1.180 0.2441
White -0.0878571 0.0514379 -1.708 0.0944 .
T1_ICV 0.0003022 0.0019042 0.159 0.8746
sqrt(INR):Male1 -0.7758651 0.3475874 -2.232 0.0305 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.174 on 46 degrees of freedom
Multiple R-squared: 0.2978, Adjusted R-squared: 0.2063
F-statistic: 3.252 on 6 and 46 DF, p-value: 0.009498
std_beta(lm10Ma)
outlierTest(lm11F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm11Ma <- lm(neg_lAG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm11Ma)
Call:
lm(formula = neg_lAG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.29983 -0.09409 -0.01208 0.07789 0.32190
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2389498 2.3780439 -0.100 0.920
sqrt(INR) -0.1232605 0.1842033 -0.669 0.507
Male1 -0.1439166 0.2995542 -0.480 0.633
T1_Age 0.0467741 0.0785866 0.595 0.555
White -0.0581777 0.0419721 -1.386 0.172
T1_ICV -0.0001192 0.0015538 -0.077 0.939
sqrt(INR):Male1 0.0465088 0.2836228 0.164 0.870
Residual standard error: 0.142 on 46 degrees of freedom
Multiple R-squared: 0.1724, Adjusted R-squared: 0.06445
F-statistic: 1.597 on 6 and 46 DF, p-value: 0.1696
std_beta(lm11Ma)
outlierTest(lm12F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm12Ma <- lm(neg_lThal_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm12Ma)
Call:
lm(formula = neg_lThal_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.33125 -0.06768 0.00319 0.07834 0.19568
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3294061 1.8121853 0.182 0.8566
sqrt(INR) -0.2209442 0.1403719 -1.574 0.1223
Male1 -0.4484302 0.2282749 -1.964 0.0555 .
T1_Age 0.0345056 0.0598868 0.576 0.5673
White 0.0491573 0.0319848 1.537 0.1312
T1_ICV -0.0003327 0.0011841 -0.281 0.7800
sqrt(INR):Male1 0.3707231 0.2161344 1.715 0.0930 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1082 on 46 degrees of freedom
Multiple R-squared: 0.2113, Adjusted R-squared: 0.1084
F-statistic: 2.054 on 6 and 46 DF, p-value: 0.0774
std_beta(lm12Ma)
outlierTest(lm13F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm13Ma <- lm(neg_lHipp_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm13Ma)
Call:
lm(formula = neg_lHipp_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.228744 -0.041216 -0.004803 0.043004 0.270573
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.761818 1.821483 -1.516 0.13630
sqrt(INR) -0.336101 0.141092 -2.382 0.02140 *
Male1 -0.618514 0.229446 -2.696 0.00978 **
T1_Age -0.023982 0.060194 -0.398 0.69217
White 0.045593 0.032149 1.418 0.16288
T1_ICV 0.002329 0.001190 1.957 0.05648 .
sqrt(INR):Male1 0.580188 0.217243 2.671 0.01043 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1088 on 46 degrees of freedom
Multiple R-squared: 0.2449, Adjusted R-squared: 0.1464
F-statistic: 2.487 on 6 and 46 DF, p-value: 0.03623
std_beta(lm13Ma)
outlierTest(lm14F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm14Ma <- lm(neg_rHipp_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm14Ma)
Call:
lm(formula = neg_rHipp_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.167577 -0.061443 0.002776 0.061129 0.140605
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.9544614 1.3971300 -2.830 0.00687 **
sqrt(INR) -0.1615196 0.1082217 -1.492 0.14240
Male1 -0.5417792 0.1759917 -3.078 0.00350 **
T1_Age 0.0329283 0.0461706 0.713 0.47933
White -0.0010871 0.0246591 -0.044 0.96503
T1_ICV 0.0026953 0.0009129 2.953 0.00495 **
sqrt(INR):Male1 0.5072121 0.1666319 3.044 0.00385 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08343 on 46 degrees of freedom
Multiple R-squared: 0.2859, Adjusted R-squared: 0.1927
F-statistic: 3.069 on 6 and 46 DF, p-value: 0.01305
std_beta(lm14Ma)
outlierTest(lm15F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm15Ma <- lm(neg_lThal_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm15Ma)
Call:
lm(formula = neg_lThal_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.33125 -0.06768 0.00319 0.07834 0.19568
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3294061 1.8121853 0.182 0.8566
sqrt(INR) -0.2209442 0.1403719 -1.574 0.1223
Male1 -0.4484302 0.2282749 -1.964 0.0555 .
T1_Age 0.0345056 0.0598868 0.576 0.5673
White 0.0491573 0.0319848 1.537 0.1312
T1_ICV -0.0003327 0.0011841 -0.281 0.7800
sqrt(INR):Male1 0.3707231 0.2161344 1.715 0.0930 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1082 on 46 degrees of freedom
Multiple R-squared: 0.2113, Adjusted R-squared: 0.1084
F-statistic: 2.054 on 6 and 46 DF, p-value: 0.0774
std_beta(lm15Ma)
outlierTest(lm16F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm16Ma <- lm(neg_FG_cs ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm16Ma)
Call:
lm(formula = neg_FG_cs ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.176275 -0.073597 0.006977 0.066086 0.222399
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.939e-01 1.596e+00 0.247 0.8062
sqrt(INR) -1.963e-01 1.236e-01 -1.588 0.1192
Male1 -4.352e-01 2.010e-01 -2.165 0.0357 *
T1_Age -9.400e-03 5.274e-02 -0.178 0.8593
White -2.424e-02 2.817e-02 -0.860 0.3941
T1_ICV -8.224e-05 1.043e-03 -0.079 0.9375
sqrt(INR):Male1 4.144e-01 1.904e-01 2.177 0.0347 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.09531 on 46 degrees of freedom
Multiple R-squared: 0.1066, Adjusted R-squared: -0.009973
F-statistic: 0.9144 on 6 and 46 DF, p-value: 0.4932
std_beta(lm16Ma)
outlierTest(lm17F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm17Ma <- lm(pos_lLG_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm17Ma)
Call:
lm(formula = pos_lLG_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.178283 -0.047509 -0.008937 0.029333 0.177946
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.171473 1.926671 1.646 0.11093
sqrt(INR) 0.442784 0.134101 3.302 0.00263 **
Male1 0.726124 0.256473 2.831 0.00849 **
T1_Age -0.083784 0.057364 -1.461 0.15527
White 0.012479 0.032578 0.383 0.70457
T1_ICV -0.002017 0.001312 -1.537 0.13558
sqrt(INR):Male1 -0.660317 0.240825 -2.742 0.01052 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.09086 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.4574, Adjusted R-squared: 0.3411
F-statistic: 3.934 on 6 and 28 DF, p-value: 0.005631
std_beta(lm17Ma)
outlierTest(lm18F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm18Ma <- lm(pos_lITG_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm18Ma)
Call:
lm(formula = pos_lITG_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.18613 -0.07960 -0.01354 0.07937 0.19463
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.1134525 2.4061682 -0.463 0.647120
sqrt(INR) 0.7083473 0.1674754 4.230 0.000226 ***
Male1 0.9299320 0.3203019 2.903 0.007125 **
T1_Age -0.0027093 0.0716409 -0.038 0.970101
White 0.0321223 0.0406858 0.790 0.436443
T1_ICV 0.0002795 0.0016391 0.171 0.865837
sqrt(INR):Male1 -0.8790167 0.3007599 -2.923 0.006796 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1135 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.4209, Adjusted R-squared: 0.2968
F-statistic: 3.392 on 6 and 28 DF, p-value: 0.01216
std_beta(lm18Ma)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm19Ma <- lm(pos_rLG_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm19Ma)
Call:
lm(formula = pos_rLG_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.209777 -0.022577 0.008025 0.033420 0.130932
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6513438 1.4778256 0.441 0.662782
sqrt(INR) 0.4051405 0.1028604 3.939 0.000495 ***
Male1 0.0746646 0.1967237 0.380 0.707150
T1_Age -0.0286914 0.0440005 -0.652 0.519674
White 0.0446099 0.0249885 1.785 0.085064 .
T1_ICV -0.0005878 0.0010067 -0.584 0.563949
sqrt(INR):Male1 -0.0847129 0.1847214 -0.459 0.650063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.06969 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.5253, Adjusted R-squared: 0.4236
F-statistic: 5.164 on 6 and 28 DF, p-value: 0.001102
std_beta(lm19Ma)
outlierTest(lm20F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm20Ma <- lm(pos_rSPL_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm20Ma)
Call:
lm(formula = pos_rSPL_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.17477 -0.07169 0.01102 0.05353 0.18477
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.484343 2.022558 -1.228 0.22955
sqrt(INR) 0.462011 0.140775 3.282 0.00277 **
Male1 0.641758 0.269237 2.384 0.02416 *
T1_Age -0.009015 0.060219 -0.150 0.88208
White -0.011287 0.034199 -0.330 0.74384
T1_ICV 0.001540 0.001378 1.118 0.27308
sqrt(INR):Male1 -0.569646 0.252810 -2.253 0.03226 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.09538 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.3469, Adjusted R-squared: 0.2069
F-statistic: 2.479 on 6 and 28 DF, p-value: 0.04751
std_beta(lm20Ma)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm21Ma <- lm(neg_rPTR_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm21Ma)
Call:
lm(formula = neg_rPTR_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.19942 -0.01818 -0.00027 0.01722 0.12794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4604586 1.3273173 1.100 0.280568
sqrt(INR) -0.3547662 0.0923846 -3.840 0.000644 ***
Male1 -0.6052391 0.1766885 -3.425 0.001913 **
T1_Age -0.0492362 0.0395193 -1.246 0.223134
White -0.0004490 0.0224436 -0.020 0.984182
T1_ICV -0.0003762 0.0009042 -0.416 0.680566
sqrt(INR):Male1 0.5601276 0.1659085 3.376 0.002172 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.06259 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.4011, Adjusted R-squared: 0.2728
F-statistic: 3.125 on 6 and 28 DF, p-value: 0.01795
std_beta(lm21Ma)
outlierTest(lm22F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm22Ma <- lm(neg_lCerebel_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm22Ma)
Call:
lm(formula = neg_lCerebel_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.10335 -0.02336 -0.00587 0.02449 0.08326
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.952e-01 1.052e+00 0.566 0.57593
sqrt(INR) -3.672e-01 7.320e-02 -5.016 2.65e-05 ***
Male1 -4.982e-01 1.400e-01 -3.559 0.00135 **
T1_Age -1.214e-02 3.131e-02 -0.388 0.70115
White -3.447e-02 1.778e-02 -1.938 0.06275 .
T1_ICV -4.535e-05 7.164e-04 -0.063 0.94998
sqrt(INR):Male1 4.634e-01 1.315e-01 3.525 0.00148 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0496 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.518, Adjusted R-squared: 0.4147
F-statistic: 5.015 on 6 and 28 DF, p-value: 0.001331
std_beta(lm22Ma)
outlierTest(lm23F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm23Ma <- lm(neg_rSFG_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm23Ma)
Call:
lm(formula = neg_rSFG_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.262848 -0.045948 0.007162 0.048026 0.226662
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9737408 2.2314265 -0.436 0.6659
sqrt(INR) -0.3907123 0.1553129 -2.516 0.0179 *
Male1 -0.5187947 0.2970408 -1.747 0.0917 .
T1_Age 0.0911934 0.0664381 1.373 0.1808
White -0.0316536 0.0377311 -0.839 0.4086
T1_ICV 0.0002696 0.0015201 0.177 0.8605
sqrt(INR):Male1 0.5007620 0.2789180 1.795 0.0834 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1052 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.3116, Adjusted R-squared: 0.164
F-statistic: 2.112 on 6 and 28 DF, p-value: 0.08357
std_beta(lm23Ma)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm24Ma <- lm(neg_lSFG_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm24Ma)
Call:
lm(formula = neg_lSFG_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.15901 -0.04040 0.01431 0.05354 0.10485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.7951785 1.5365576 1.819 0.07960 .
sqrt(INR) -0.5155758 0.1069483 -4.821 4.53e-05 ***
Male1 -0.6778312 0.2045419 -3.314 0.00255 **
T1_Age 0.0529327 0.0457492 1.157 0.25704
White -0.0002376 0.0259816 -0.009 0.99277
T1_ICV -0.0021157 0.0010467 -2.021 0.05290 .
sqrt(INR):Male1 0.6071040 0.1920626 3.161 0.00376 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07246 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.5445, Adjusted R-squared: 0.4469
F-statistic: 5.579 on 6 and 28 DF, p-value: 0.0006574
std_beta(lm24Ma)
outlierTest(lm25F)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm25Ma <- lm(neg_lSLF_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm25Ma)
Call:
lm(formula = neg_lSLF_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.106059 -0.016623 0.000047 0.032360 0.067213
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.174e-01 9.249e-01 0.235 0.815858
sqrt(INR) -2.704e-01 6.438e-02 -4.200 0.000245 ***
Male1 -3.747e-01 1.231e-01 -3.043 0.005050 **
T1_Age -1.746e-03 2.754e-02 -0.063 0.949888
White -2.153e-02 1.564e-02 -1.376 0.179627
T1_ICV 7.871e-05 6.301e-04 0.125 0.901476
sqrt(INR):Male1 3.653e-01 1.156e-01 3.160 0.003767 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.04362 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.4394, Adjusted R-squared: 0.3193
F-statistic: 3.658 on 6 and 28 DF, p-value: 0.008302
std_beta(lm25Ma)
contrasts(vols$Male) = c(1, 0)
#SE male
lm26M <- lm(neg_rHipp_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols)
summary(lm26M)
Call:
lm(formula = neg_rHipp_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols)
Residuals:
Min 1Q Median 3Q Max
-0.187588 -0.028662 -0.004849 0.034577 0.137558
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.662e-01 5.541e-01 0.300 0.764818
sqrt(INR) -2.395e-01 7.220e-02 -3.317 0.001269 **
Male1 -3.084e-01 9.393e-02 -3.284 0.001412 **
T1_Age 3.351e-03 6.421e-03 0.522 0.602955
White -1.613e-02 1.258e-02 -1.282 0.202856
T1_ICV 3.696e-05 4.107e-04 0.090 0.928473
sqrt(INR):Male1 3.187e-01 8.866e-02 3.595 0.000506 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.06213 on 100 degrees of freedom
(40 observations deleted due to missingness)
Multiple R-squared: 0.1557, Adjusted R-squared: 0.1051
F-statistic: 3.075 on 6 and 100 DF, p-value: 0.008368
std_beta(lm26M)
contrasts(vols_match$Male) = c(1, 0)
#SE male
lm26Ma <- lm(neg_rHipp_lg ~ sqrt(INR) * Male + T1_Age + White + T1_ICV, data = vols_match)
summary(lm26Ma)
Call:
lm(formula = neg_rHipp_lg ~ sqrt(INR) * Male + T1_Age + White +
T1_ICV, data = vols_match)
Residuals:
Min 1Q Median 3Q Max
-0.12769 -0.02291 0.00175 0.02237 0.13387
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.0169430 1.2465803 -0.816 0.4215
sqrt(INR) -0.2214022 0.0867651 -2.552 0.0165 *
Male1 -0.3683556 0.1659410 -2.220 0.0347 *
T1_Age 0.0061051 0.0371155 0.164 0.8705
White -0.0253821 0.0210784 -1.204 0.2386
T1_ICV 0.0008779 0.0008492 1.034 0.3101
sqrt(INR):Male1 0.3932683 0.1558168 2.524 0.0176 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05878 on 28 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.3814, Adjusted R-squared: 0.2488
F-statistic: 2.877 on 6 and 28 DF, p-value: 0.02597
std_beta(lm26Ma)
residualize_models_cs <-
vols %>%
gather(region, volume, pos_rSTG_cs:neg_lAG_cs) %>%
group_by(region) %>%
nest() %>%
mutate(
residuals = map(
data,
~lm(volume ~ T1_ICV + White + T1_Age, data = .)$residuals
)
) %>%
unnest() %>%
mutate(
region = as.factor(region)
)
residualize_models_lg <-
vols %>%
filter(!is.na(pos_rSPL_lg)) %>%
gather(region, volume, pos_rSPL_lg:neg_rPTR_lg) %>%
group_by(region) %>%
nest() %>%
mutate(
residuals = map(
data,
~lm(volume ~ T1_ICV + White + T1_Age + Interval, data = .)$residuals
)
) %>%
unnest() %>%
mutate(
region = as.factor(region)
)
residualize_models_cs_agematch <-
vols_match %>%
gather(region, volume, pos_rSTG_cs:neg_lAG_cs) %>%
group_by(region) %>%
nest() %>%
mutate(
residuals = map(
data,
~lm(volume ~ T1_ICV + White + T1_Age, data = .)$residuals
)
) %>%
unnest() %>%
mutate(
region = as.factor(region)
)
residualize_models_lg_agematch <-
vols_match %>%
filter(!is.na(pos_rSPL_lg)) %>%
gather(region, volume, pos_rSPL_lg:neg_rPTR_lg) %>%
group_by(region) %>%
nest() %>%
mutate(
residuals = map(
data,
~lm(volume ~ T1_ICV + White + T1_Age + Interval, data = .)$residuals
)
) %>%
unnest() %>%
mutate(
region = as.factor(region)
)
residualize_models_cs <-
residualize_models_cs %>%
mutate(
region_named = factor(
region,
levels = c(
"pos_rCV_cs",
"pos_rMB_cs",
"pos_rPTR_cs",
"pos_rSFG_cs_motor",
"pos_rPCG",
"pos_rSFG_cs_dlPFC",
"pos_rMFG_cs",
"pos_lLOC_cs",
"pos_lIFG_cs",
"pos_rSTG_cs",
"neg_lAG_cs",
"neg_rThal_cs",
"neg_lHipp_cs",
"neg_rHipp_cs",
"neg_lThal_cs",
"neg_FG_cs"
),
labels = c(
"Right CV (3, -47, -35)",
"Right midbrain (3, -32, -4)",
"Right PTR (40, -55, 2)",
"Right SFG (9, -8, 54)",
"Right PoG (15, -32, 69)",
"Right SFG (11, 46, 45)",
"Right MFG (51, 38, 5)",
"Left LOG (-23, -89, 3)",
"Left IFG (-48, 46, -8)",
"Right STG (63, -47, 19)",
"Left AG (-46, -39, 33)",
"Right thalamus (13, -23, 8)",
"Left CGH (-29, -19, -33)",
"Right CGH (23, -14, -23)",
"Left thalamus (-9, -24, 10)",
"Left FG (-49, -49, -16)"
)
),
Sex = factor(
Male,
levels = c(0, 1),
labels = c("Female", "Male")
)
)
glimpse(residualize_models_cs_agematch)
Observations: 848
Variables: 24
$ region [3m[38;5;246m<fct>[39m[23m pos_rSTG_cs, pos_rSTG_cs, pos_rSTG_cs, pos_rSTG_cs, pos_rSTG_cs, pos_rSTG…
$ residuals [3m[38;5;246m<dbl>[39m[23m -0.029977783, 0.179923920, 0.314162377, 0.035453172, -0.149856670, 0.0996…
$ ID [3m[38;5;246m<dbl>[39m[23m 18, 22, 23, 25, 32, 35, 39, 42, 43, 46, 48, 49, 54, 60, 64, 65, 75, 76, 7…
$ T1_ICV [3m[38;5;246m<dbl>[39m[23m 1352.300, 1317.275, 1359.031, 1347.595, 1327.863, 1339.837, 1338.875, 134…
$ White [3m[38;5;246m<dbl>[39m[23m 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0…
$ T1_Age [3m[38;5;246m<dbl>[39m[23m 11.09, 11.22, 11.94, 11.67, 11.53, 11.74, 11.22, 11.95, 11.70, 11.65, 11.…
$ Sex [3m[38;5;246m<fct>[39m[23m Female, Male, Female, Male, Female, Male, Female, Male, Female, Male, Mal…
$ INR [3m[38;5;246m<dbl>[39m[23m 1.2144654, 1.1273735, 1.0160947, 0.7463322, 1.0160947, 1.4021141, 1.27994…
$ INR_Sex [3m[38;5;246m<dbl>[39m[23m 1.2144654, 2.2547470, 1.0160947, 1.4926645, 1.0160947, 2.8042282, 1.27994…
$ pos_rSPL_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.007105, 0.014721, -0.006227, NA, 0.074150, NA, -0.085309, 0.076353,…
$ pos_rLG_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.011626, -0.095641, -0.064236, NA, 0.044319, NA, 0.021908, -0.063848…
$ pos_lITG_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.096546, 0.092174, -0.192735, NA, -0.061005, NA, 0.274067, -0.080808…
$ pos_lLG_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.027371, -0.100991, 0.026319, NA, 0.135742, NA, -0.001221, -0.140797…
$ neg_rHipp_lg [3m[38;5;246m<dbl>[39m[23m NA, -0.180199, 0.011528, 0.066754, NA, -0.023231, NA, 0.054839, 0.072891,…
$ neg_lSLF_lg [3m[38;5;246m<dbl>[39m[23m NA, -0.025449, 0.029119, 0.103692, NA, -0.113493, NA, -0.045815, 0.046227…
$ neg_lSFG_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.121833, -0.134355, 0.221174, NA, -0.026029, NA, -0.067296, 0.053502…
$ neg_rSFG_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.031358, 0.300343, 0.073874, NA, 0.022223, NA, 0.059198, 0.063420, 0…
$ neg_lCerebel_lg [3m[38;5;246m<dbl>[39m[23m NA, -0.041313, -0.006640, 0.018558, NA, -0.146236, NA, -0.078917, 0.02951…
$ neg_rPTR_lg [3m[38;5;246m<dbl>[39m[23m NA, 0.031497, -0.236286, -0.006042, NA, -0.042279, NA, -0.008907, 0.02144…
$ Interval [3m[38;5;246m<dbl>[39m[23m NA, 1.67, 1.88, 1.95, NA, 2.58, NA, 1.82, 1.78, 1.22, 2.49, 1.77, 1.80, 1…
$ Male [3m[38;5;246m<fct>[39m[23m 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0…
$ age_matched [3m[38;5;246m<dbl>[39m[23m 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ volume [3m[38;5;246m<dbl>[39m[23m -0.076392, 0.022881, 0.095106, -0.124102, -0.271639, -0.069962, 0.057077,…
$ region_named [3m[38;5;246m<fct>[39m[23m "Right STG (63, -47, 19)", "Right STG (63, -47, 19)", "Right STG (63, -47…
residualize_models_cs %>%
filter(str_detect(region, "neg")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Volume in early adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/neg_cross-sectional.png",
height = 11,
width = 13
)
residualize_models_cs_agematch %>%
filter(str_detect(region, "neg")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Volume in early adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/neg_cross-sectional_agematch.png",
height = 11,
width = 13
)
residualize_models_cs %>%
filter(str_detect(region, "pos")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
#scale_x_continuous(breaks = seq.int(0, 2, .2)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Volume in early adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/pos_cross-sectional.png",
height = 12,
width = 15.5
)
residualize_models_cs_agematch %>%
filter(str_detect(region, "pos")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
#scale_x_continuous(breaks = seq.int(0, 2, .2)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 16),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Volume in early adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/pos_cross-sectional_agematch.png",
height = 12,
width = 15.5
)
residualize_models_lg <-
residualize_models_lg %>%
mutate(
region_named = factor(
region,
levels = c(
"pos_lLG_lg",
"pos_lITG_lg",
"pos_rLG_lg",
"pos_rSPL_lg",
"neg_rPTR_lg",
"neg_lCerebel_lg",
"neg_rSFG_lg",
"neg_lSFG_lg",
"neg_lSLF_lg",
"neg_rHipp_lg"
),
labels = c(
"Left LG (-20, -75, -5)",
"Left ITG (-48, 1, -40)",
"Right LG (12, -73, -9)",
"R SPL (27, -60, 56)",
"Right PTR (24, -70, 7)",
"Left cerebellum (-18, -66, -21)",
"Right SFG (17, -5, 58)",
"Left SFG (-16, 62, -10)",
"Left SLF (-33, -44, 16)",
"Right CGH (26, -33, -7)"
)
),
Sex = factor(
Male,
levels = c(0, 1),
labels = c("Female", "Male")
)
)
residualize_models_lg_agematch <-
residualize_models_lg_agematch %>%
mutate(
region_named = factor(
region,
levels = c(
"pos_lLG_lg",
"pos_lITG_lg",
"pos_rLG_lg",
"pos_rSPL_lg",
"neg_rPTR_lg",
"neg_lCerebel_lg",
"neg_rSFG_lg",
"neg_lSFG_lg",
"neg_lSLF_lg",
"neg_rHipp_lg"
),
labels = c(
"Left LG (-20, -75, -5)",
"Left ITG (-48, 1, -40)",
"Right LG (12, -73, -9)",
"R SPL (27, -60, 56)",
"Right PTR (24, -70, 7)",
"Left cerebellum (-18, -66, -21)",
"Right SFG (17, -5, 58)",
"Left SFG (-16, 62, -10)",
"Left SLF (-33, -44, 16)",
"Right CGH (26, -33, -7)"
)
),
Sex = factor(
Male,
levels = c(0, 1),
labels = c("Female", "Male")
)
)
residualize_models_lg %>%
filter(str_detect(region, "neg")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Change in volume from earlier to later adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/neg_longitudinal.png",
height = 11,
width = 13
)
residualize_models_lg_agematch %>%
filter(str_detect(region, "neg")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Change in volume from earlier to later adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/neg_longitudinal_agematch.png",
height = 11,
width = 13
)
residualize_models_lg %>%
filter(str_detect(region, "pos")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Change in volume from earlier to later adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/pos_longitudinal.png",
height = 11,
width = 13
)
residualize_models_lg_agematch %>%
filter(str_detect(region, "pos")) %>%
ggplot(aes(INR, residuals, color = Sex, fill = Sex)) +
geom_point(size = 3, alpha = 1/2) +
geom_smooth(method = "lm", formula = y ~ sqrt(x), size = 2) +
scale_x_continuous(breaks = seq.int(0, 2, .25)) +
scale_color_manual(
values = c("darkred", "royalblue4")
) +
scale_fill_manual(
values = c("darkred", "royalblue4")
) +
theme_minimal() +
theme(
legend.title = element_blank(),
axis.title = element_text(size = 24),
axis.text = element_text(size = 18),
legend.text = element_text(size = 22),
strip.text = element_text(size = 18),
legend.key.size = unit(2, 'lines')
) +
facet_wrap(.~region_named) +
labs(
y = "Change in volume from earlier to later adolescence\n(residuals)",
x = "Family income-to-needs ratio"
)
ggsave(
"~/Desktop/ELS/income_TBM/income_TBM_sync/plots/pos_longitudinal_agematch.png",
height = 11,
width = 13
)